A diagnosis of Parkinson disease (PD) is a profound and life-changing event for a patient. However diagnosis of PD, particularly early in the course of illness is difficult for a variety of reasons. For example, individuals with PD will often preset with a fragment of the full clinical syndrome. Further, a number of disorders with very different prognoses have symptoms that overlap with the symptoms of PD. Even when diagnosis is firm, sub- populations within the broad disease specific classification of PD have been clinically observed, such as individuals with tremor dominant disease (TD-PD) vs. those with predominant postural instability and gait disorder (PIGD-PD). These sub-populations have distinct differences in symptoms and rate of progression. Therefore, prognosis for individuals with a diagnosis of Parkinson disease varies dramatically from one individual to another. This project evaluates the utility of diffusion tensor imaging (DTI) as a method to improve diagnosis of PD. We compare DTI to Ioflupane I123 SPECT (DaTscan). The DaTscan is a nuclear medicine modality that been approved to aid in diagnosis of Parkinsonism. This test can determine whether there is a defect in brain dopamine systems, but cannot distinguish between PD and other causes of Parkinsonism, or identify subsets within those with PD. Moreover, DaTscan is expensive, has some limitations in availability, and involves exposure to radioactive iodine, which has been raised as a concern. It has been a general thesis of the investigator that information dense MR images have sufficient embedded information to generate disease-specific diagnostic maps. Our lab uses high performance computing to compensate for individual subject variability in brain scans, and extract diagnostic signals. The PI has published data showing that resting fMRI can segregate individuals with PD from healthy controls with 92% sensitivity and 87% specificity. Further development of statistical techniques, in collaboration with colleagues in the UAB department of statistics, has resulted in development of a method that is able to generate a map using Diffusion Tensor Imaging (DTI) that can predict group membership (PD or Control) of subjects left out of our analysis with a high sensitivity and specificity. Our group is adapting his diagnostic methods, which provide reliable, subject-specific classification, as a potent tool for scientific discovery of regions reliably affected early in PD. This project will evaluate the utility of DTI as an adjunctive method to improve early diagnosis of PD. We propose DTI will provide a superior sensitivity and specificity to DaTscan for early diagnosis of PD (as opposed to Parkinsonism). We propose findings on DTI will differ in individuals with tremor predominant disease (TD- PD) compared to those with prominent postural instability and gait disorder (PIGD-PD). We will evaluate two populations in this study: 1) a local group drawn from individuals with uncertain PD diagnosis referred for clinical DaTscan, and 2) individuals with well characterized PD based on established consensus criteria, drawn from the Parkinson's Progression Markers Initiative (PPMI) population. We have distinct hypotheses surrounding each population group within the study. For group 1, we will compare the sensitivity and specificity of a clinical DaTscan with a baseline MRI for identificatio of a dopamine deficient state, and prediction of final diagnosis at 36 months. Group 2 from the PPMI dataset includes a control population, and individuals with early, well characterized PD (clinical characterization of all subjects, including controls, includes a clinical DaTscan). DTI i this case has occurred at multiple sites, using a defined protocol. We evaluate a number of DTI measures in group 2, including tensor-based morphometry (TBM) as a method to improve diagnostic precision, as well as the relationship between atrophy and hypertrophy of particular fiber tracts and disease progression. In addition, we evaluate the relationship of disease phenotype (TD-PD vs. PIGD-PD) to DTI measures, and the capacity of DTI to predict disease phenotype.